Medical & Biological Engineering & Computing

, Volume 45, Issue 11, pp 1045–1051 | Cite as

On the radiobiological impact of metal artifacts in head-and-neck IMRT in terms of tumor control probability (TCP) and normal tissue complication probability (NTCP)

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Abstract

To investigate the effects of distorted head-and-neck (H&N) intensity-modulated radiation therapy (IMRT) dose distributions (hot and cold spots) on normal tissue complication probability (NTCP) and tumor control probability (TCP) due to dental-metal artifacts. Five patients’ IMRT treatment plans have been analyzed, employing five different planning image data-sets: (a) uncorrected (UC); (b) homogeneous uncorrected (HUC); (c) sinogram completion corrected (SCC); (d) minimum-value-corrected (MVC); and (e) streak-artifact-reduction including minimum-value-correction (SAR-MVC), which has been taken as the reference data-set. The effects on NTCP and TCP were evaluated using the Lyman-NTCP model and the Logistic-TCP model, respectively. When compared to the predicted NTCP obtained using the reference data-set, the treatment plan based on the original CT data-set (UC) yielded an increase in NTCP of 3.2 and 2.0% for the spared parotid gland and the spinal cord, respectively. While for the treatment plans based on the MVC CT data-set the NTCP increased by a 1.1% and a 0.1% for the spared parotid glands and the spinal cord, respectively. In addition, the MVC correction method showed a reduction in TCP for target volumes (MVC: ΔTCP = −0.6% vs. UC: ΔTCP = -1.9%) with respect to that of the reference CT data-set. Our results indicate that the presence of dental-metal-artifacts in H&N planning CT data-sets has an impact on the estimates of TCP and NTCP. In particular dental-metal-artifacts lead to an increase in NTCP for the spared parotid glands and a slight decrease in TCP for target volumes.

Keywords

Metal artifacts Treatment planning Head and neck cancer TCP NTCP 
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Notes

Acknowledgments

This work was partially supported by a research grant for Philips Radiation Oncology Systems.

References

  1. 1.
    Bal M, Spies L (2006) Metal artifact reduction in CT using tissue-class modeling and adaptive prefiltering. Med Phys 33:2852–2859CrossRefGoogle Scholar
  2. 2.
    Bentzen SM (2002) Dose-response relationships in radiotherapy. In: Steel GG (ed) Basic clinical radiobiology. Hodder Arnold, Great Britain, pp 94–104Google Scholar
  3. 3.
    Bentzen SM, Thames HD (1996) Tumor volume and local control probability: clinical and radiobiological interpretations. Int J Radiat Oncol Biol Phys 36:247–251CrossRefGoogle Scholar
  4. 4.
    Burman C, Kutcher GJ, Emami B, Goitein M (1991) Fitting of normal tissue tolerance data to an analytic function. Int J Radiat Oncol Biol Phys 21:129–135Google Scholar
  5. 5.
    Eisbruch A, Ship JA, Martel MK et al (1996) Parotid gland sparing in patients undergoing bilateral head and neck irradiation: techniques and early results. Int J Radiat Oncol Biol 30(2):469–480CrossRefGoogle Scholar
  6. 6.
    Eisbruch A, Ten Haken RK, Kim HM, Marsh LH, Ship JA (1999) Dose, volume, and function relationships in parotid salivary glands following conformal and intensity-modulated irradiation of head and neck cancer. Int J Radiat Oncol Biol 45(3):577–587CrossRefGoogle Scholar
  7. 7.
    Evans RD (1985) The atomic nucleus, reprint edition 1982 of 14th printing 1972. Krieger Malabar, Florida, USAGoogle Scholar
  8. 8.
    Fenwick JD (1998) Predicting the radiation control probability of heterogeneous tumor ensembles: data analysis and parameter estimation using a closed-form expression. Phys Med Biol 43:2159–2178CrossRefGoogle Scholar
  9. 9.
    Fenwick JD, Tomé WA, Soisson ET, Mehta MP, Mackie TR (2006) Tomotherapy and other innovative IMRT delivery systems. Semin Radiat Oncol 16:199–208CrossRefGoogle Scholar
  10. 10.
    Goitein M (1987) The probability of controlling an inhomogeneously irradiated tumor. In: Zink S (ed) Report of the working group on the evaluation of treatment planning for particle beam radiotherapy. National Cancer Institute, Bethesda, MarylandGoogle Scholar
  11. 11.
    Goitein M, Niemierko A, Okunieff P (1995) In: Kaulner K, Carey B, Crellin A, Harrison RM, (eds) Quantitative imaging in oncology. In: Proceedings of the 19th LH gray conference, Br Inst Radiol., London, pp 25–32Google Scholar
  12. 12.
    Holthusen H (1936) Erfahrungen über die Verträglichkeitsgrenze für Röntgenstrahlen und deren Nutzanwendung zur Verhütung von Schäden. Strahlentherapie 57:254–269Google Scholar
  13. 13.
    Hong TS, Tomé WA, Chappell RJ et al (2005) The impact of daily setup variations on head-and-neck intensity-modulated radiation therapy. Int J Radiat Oncol Biol Phys 61(3):779–788CrossRefGoogle Scholar
  14. 14.
    Kim Y, Tomé WA (2006) Risk-adaptive optimization: selective boosting of high-risk tumor subvolumes. Int J Radiat Oncol Biol Phys 66:1528–1542CrossRefGoogle Scholar
  15. 15.
    Kim Y, Tomé WA, Matthieu B, Todd RM, Lothar S (2006) The impact of dental metal artifacts on head and neck IMRT dose distributions. Radiother Oncol 79:198–202CrossRefGoogle Scholar
  16. 16.
    Kutcher GJ, Burman C (1989) Calculation of complication probability factors for non-uniform normal tissue irradiation: the effective volume method. Int J Radiat Oncol Biol Phys 16:1623–1630Google Scholar
  17. 17.
    Langen KM, Meeks SL, Poole DO, et al (2005) The use of megavoltage CT (MVCT) images for dose recomputations. Phys Med Biol 50:4259–4279CrossRefGoogle Scholar
  18. 18.
    Lyman JT (1985) Complication probability-as assessed from dose-volume histograms. Radiat Res 104:S13–S19CrossRefGoogle Scholar
  19. 19.
    Marks JE, Bedwinek JM, Lee F, Purdy JA, Perez CA (1982) Dose-response analysis for nasopharyngeal carcinoma: an historical perspective. Cancer 50:1042–1050CrossRefGoogle Scholar
  20. 20.
    Munro TR, Gilbert CW (1961) The relation between tumour lethal doses and the radiosensitivity of tumour cells. Br J Radiol 34:246–251CrossRefGoogle Scholar
  21. 21.
    Nahum AE, Tait DM (1992) Maximizing local control by customized dose prescription for pelvic tumours. In: Breit A (ed) Adv. radiation therapy response monitoring and treatment planning. Springer, Berlin, pp 425–431Google Scholar
  22. 22.
    Niemierko A, Goitein M (1993) Implementation of a model for estimating tumor control probability for an inhomogeneously irradiated tumor. Radiother Oncol 29:140–147CrossRefGoogle Scholar
  23. 23.
    Okunieff P, Morgan D, Niemierko A et al (1995) Radiation dose-response of human tumors. Int J Radiat Oncol Biol Phys 32:1227–1237CrossRefGoogle Scholar
  24. 24.
    Olive CS, Kaus MR, Pekar V et al (2004) Segmentation-aided adaptive filtering for metal artifacts reduction in radio-therapeutic CT images. Proc of SPIE 5370:1991–2002CrossRefGoogle Scholar
  25. 25.
    Orton NP, Tomé WA (2004) The impact of daily shifts on prostate IMRT dose distributions. Med Phys 31(10):2845–2848CrossRefGoogle Scholar
  26. 26.
    Popple RA, Ove R, Shen S (2002) Tumor control probability models for selective boosting of hypoxic subvolumes, including the effect of reoxygenation. Int J Radiat Oncol Biol Phys 54:921–927CrossRefGoogle Scholar
  27. 27.
    Roesink JM, Moerland MA, Battermann JJ et al (2001) Quantitative dose-volume response analysis of changes in parotid gland function after radiotherapy in the head-and-neck region. Int J Radiat Oncol Biol Phys 51:938–946CrossRefGoogle Scholar
  28. 28.
    Shepard DM, Olivera GH, Reckwerdt PJ, Mackie TR (2000) Iterative approaches to dose optimization in tomotherapy. Phys Med Biol 45:69–90CrossRefGoogle Scholar
  29. 29.
    Suit HD, Shalek RJ, Wette R (1965) Radiation response of C3H mouse mammary carcinoma evaluated in terms of cellular radiation sensitivity. In: Cellular radiation biology. Williams & Wilkins, Baltimore, pp 514–530Google Scholar
  30. 30.
    Suit H, Skates S, Taghain A, Okunieff P, Efird JT (1992) Clinical implications of heterogeneity of tumor response to radiation therapy. Radiother Oncol 25:251–260CrossRefGoogle Scholar
  31. 31.
    Tomé WA, Fowler JF (2002) On cold spots in tumor subvolumes. Med Phys 29:1590–1598CrossRefGoogle Scholar
  32. 32.
    Tomé WA, Fowler JF (2003) On the inclusion of the proliferation term into tumour control probability calculations for inhomogeneously irradiated tumours. Phys Med Biol 48:N261–N268CrossRefGoogle Scholar
  33. 33.
    Tomé WA, Meeks SL, McNutt RT, Buatti JM, Bova FJ, Friedman WA, Mehta M (2001) Radiother Oncol 61:33–44CrossRefGoogle Scholar
  34. 34.
    Vannier MW, Hildebolt CF, Conover G, Knapp RH, Yokoyama-Crothers N, Wang G (1997) Three-dimensional dental imaging by spiral CT. A progress report. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 84:561–570CrossRefGoogle Scholar
  35. 35.
    Vikram B, Mishra UB, Strong EW, Manolatos S (1985) Patterns of failure in carcinoma of the nasopharynx: I. Failure at the primary site. Int J Radiat Oncol Biol Phys 11:1455–1459Google Scholar
  36. 36.
    Watzke O, Kalender WA (2004) A pragmatic approach to metal artifacts reduction in CT: merging of metal artifacts reduced images. Eur Radiol 14:849–856CrossRefGoogle Scholar
  37. 37.
    Webb S, Nahum AE (1993) The biological effect of inhomogeneous tumor irradiation with inhomogeneous clonogenic cell density. Phys Med Biol 38:653–666CrossRefGoogle Scholar
  38. 38.
    Zagras GK, Shultheiss TE, Peters LJ (1987) Inter-tumour heterogeneity and radiation dose control curves. Radiother Oncol 8:353–362CrossRefGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2007

Authors and Affiliations

  1. 1.Department of Medical PhysicsUniversity of WisconsinMadisonUSA
  2. 2.Department of Human OncologyUniversity of Wisconsin Medical SchoolMadisonUSA

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